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Prepare an academic CV for faculty, postdoctoral, or research applications. Organize publications, grants, teaching, and research experience, with section order and formatting adapted to the role and academic field.
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Plan how to introduce AI into economic research, teaching, or policy analysis. Identify suitable tasks, choose tools with privacy and licensing in mind, and design reproducible data workflows with output checks and ongoing model monitoring.
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Use a teacher's recordings or lecture materials to identify evidenced teaching habits, including phrasing, examples, pacing, and visual presentation. Apply those habits to a course on a requested topic, keeping its subject facts separate from the source lesson.
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Turn student course evaluations (free-text + numeric) into an actionable teaching-improvement plan — the teaching analogue of /respond-to-referees. Clusters comments into themes, separates signal from noise, classifies each theme Keep / Change / Investigate / Out-of-scope, and drafts concrete changes mapped to the syllabus and slide decks. Use when user says "respond to my evals", "what do these course evaluations tell me", "turn my teaching feedback into a plan", or after a semester's evals arrive.
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Generate follow-up teaching moves that extend student thinking after a specific classroom response. Use when a student says something worth exploring and the teacher wants to deepen the dialogue.
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Tier vocabulary from a text or topic into everyday, academic, and technical categories with teaching priorities. Use when pre-teaching vocabulary or identifying language barriers in a text.
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Tier vocabulary from a text or topic into everyday, academic, and technical categories with teaching priorities. Use when pre-teaching vocabulary or identifying language barriers in a text.
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Generate follow-up teaching moves that extend student thinking after a specific classroom response. Use when a student says something worth exploring and the teacher wants to deepen the dialogue.
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Teaching home-economics skills as a durable practice across generations and capacity levels. Covers scaffolded task sequencing, the one-in-one-out rule, the apprenticeship model, learning by failure tolerance, family retros, the edible schoolyard framework, and the difference between teaching a recipe and teaching a kitchen. Use when designing a home-ec curriculum for children or adults, diagnosing why a taught skill is not being practiced, or building a household practice that survives the departure of the teacher.
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Use for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.
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Create a practice-area guide for a legal clinic that sets intake questions, how much help AI gives students, supervisor review requirements, checks using other plugins, and local rules. The guide configures how student-facing skills behave.
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Turn a research paper into teaching materials — a lecture outline, the 3-5 results worth presenting (with intuition), a slide skeleton ready for `/create-lecture`, discussion questions, and a problem-set brief. Reads the paper end-to-end and pitches to a stated audience level. Use when user says "turn this paper into a lecture", "teach from this paper", "build slides from this PDF", "make teaching materials from X", "I'm presenting this paper to my class".
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The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.
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Use when user asks about 藏传佛教, 噶举派, 大手印, phyag chen, 拙火, tummo, 那洛六法, 苦行, 闭关, 道歌, mgur, 米拉日巴, 玛尔巴, 上师瑜伽, 出离, 暇满, 中阴, 气脉明点, 觉受, nyams, 本觉, rig pa, or wants teaching in 米拉日巴尊者 Milarepa's voice. Triggers include "米拉日巴"、"密勒日巴"、"Milarepa"、"道歌"、"十万歌集"、"大手印"、"拙火"、"那洛六法"、"玛尔巴"、"噶举"、"白教"、"山洞修行"、"苦行"、"上师瑜伽"、"中阴"、"明空" — invoke whenever user's question touches Tibetan Kagyu / Mahāmudrā / yogi practice or asks about Milarepa's life and teachings, even without explicit request.
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Use when the user wants to teach / learn an English word as a video — turn a single English word into a self-contained HLS "supercut" lesson built from the mira video base. Stitches every season2 clip where the word is spoken (via the search-app API) into one .m3u8, prepended with a Claude-written bilingual word-intro card (word + IPA + 中文 gloss + usage, Volcano TTS) and appended with a 关注王建硕 CTA card. No MP4 burn. Triggers — "teach <word>", "讲讲 <word>", "学英语 <word>", "把 <word> 做成视频", "/wjs-teaching-english <word>".
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The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.
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Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.
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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.
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Use when a concept is too abstract for the learner's current level and needs analogy-first teaching before complexity re-injection.
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Use this skill when writing or editing Japanese documentation, teaching material, comments, or explanations for this template (Sabakyan style). Trigger words: ドキュメント執筆, 教材, 解説を書く, 日本語スタイル, README 作成, 説明文, サバキャン, わかりやすく書く.
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Detect when user is teaching best practices and persist as skills
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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.
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将科学原理、物体结构和工作流程整理为简洁的SVG图，统一配色与留白，自动适应明暗模式，并输出可在浏览器直接打开的单个HTML文件。
将文字或资料制作成比较图、时间线、流程图等信息图。结合21种布局与21种视觉风格，分析内容、设计结构，并保存图像生成提示词和成品图像。